yangs first lecture ppt
TRANSCRIPT
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By Yang By Yang By Yang By Yang CaoCaoCaoCao
BESTBESTBESTBEST----FITFITFITFIT
solutionsolutionsolutionsolution
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CorrelationCorrelationCorrelationCorrelation
RegressionRegressionRegressionRegression
WeightsWeightsWeightsWeights
LinearizationLinearizationLinearizationLinearization
LeastLeastLeastLeast----Square solutionSquare solutionSquare solutionSquare solution
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Learn:Learn:Learn:Learn:
What & HowWhat & HowWhat & HowWhat & How
for each termfor each termfor each termfor each term
twicetwicetwicetwice
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Then you can calculate :Then you can calculate :Then you can calculate :Then you can calculate :
BestBestBestBest----fit linefit linefit linefit line
Weighted MeanWeighted MeanWeighted MeanWeighted Mean
LinearizationLinearizationLinearizationLinearization
CoefficientCoefficientCoefficientCoefficient
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Correlation :Correlation :Correlation :Correlation :
Association Association Association Association
between between between between
variables.variables.variables.variables.
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Direction Direction Direction Direction
positivepositivepositivepositive negativenegativenegativenegative
XXXX YYYY XXXX YYYY XXXX YYYY XXXX YYYY
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Strength Strength Strength Strength
High Strong:High Strong:High Strong:High Strong:
Few exceptions Few exceptions Few exceptions Few exceptions
ModerateModerateModerateModerate
Low Weak:Low Weak:Low Weak:Low Weak:
Many exceptions Many exceptions Many exceptions Many exceptions
1.001.001.001.00
0.800.800.800.80
0.400.400.400.40
0000
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Correlation Coefficient: Correlation Coefficient: Correlation Coefficient: Correlation Coefficient: rrrr
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Regression :Regression :Regression :Regression :
find a formula that can find a formula that can find a formula that can find a formula that can
be used to relate two be used to relate two be used to relate two be used to relate two
variables.variables.variables.variables.
y=mx+b
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correlation V.S. regression correlation V.S. regression correlation V.S. regression correlation V.S. regression
Correlation: relationship
between variables.
Regression: finding a formula
that represents the relationship
so as to do prediction
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residual
Residual = Actual – Predicted
The regression equation or formula meets the
"least Square" criterion: the sum of square of
the residual is at its minimum.
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Weighted mean
• some data points contribute more than othersformula
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linearizelinearizelinearizelinearize : : : : make linear or get into a linear form.make linear or get into a linear form.make linear or get into a linear form.make linear or get into a linear form.
y
x0 x a=
( ) ( )f x f a=
We call the equation of the tangent
the linearization of the function.
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nx ( )nf xn ( )nf x′
( )
( )1
n
n n
n
f xx x
f x+ = −
′
Find where crosses .3y x x= − 1y =
31 x x= − 3
0 1x x= − − ( ) 31f x x x= − − ( ) 2
3 1f x x′ = −
0 1 1− 21
1 1.52
−− =
1 1.5 .875 5.75.875
1.5 1.34782615.75
− =
2 1.3478261 .1006822 4.4499055 1.3252004
( )3
1.3252004 1.3252004 1.0020584− = 1≈→
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Q?
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http://www.nvcc.edu/home/elanthier/methods/correlation.htm
http://www.pindling.org/Math/Statistics/Textbook/Chapter3_Re
gression_Correlation/Chapter3_Regres_Corr_Overview.htm
http://graphpad.com/curvefit/linear_regression.htm
http://www.answers.com/topic/weighted-mean
4.5: Linear Approximations, Differentials. and Newton's
Method. Greg Kelly, Hanford High School, Richland, Washington.